Triple
T4063538
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | I like to see it lap the Miles |
E86270
|
entity |
| Predicate | comparesTrainTo |
P278
|
FINISHED |
| Object | animal |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: animal | Statement: [I like to see it lap the Miles, comparesTrainTo, animal]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: comparesTrainTo Context triple: [I like to see it lap the Miles, comparesTrainTo, animal]
-
A.
isComparedTo
chosen
Indicates that one entity is evaluated or measured in relation to another to highlight similarities, differences, or relative qualities.
-
B.
trainingCompute
Indicates the amount or configuration of computational resources used to train a model or system.
-
C.
trainControl
Indicates that one entity exercises authority over or manages the operation, direction, or behavior of another entity in a training or instructional context.
-
D.
comparisonSet
Indicates that one entity is part of a group of entities that are being compared or evaluated against each other.
-
E.
trainingUse
Indicates that something is used for training purposes, such as preparing, educating, or improving the skills or performance of an entity.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69aed93c69208190a4efac0efe3cd69b |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefd0bdea48190805a79515ee92709 |
completed | March 9, 2026, 5:02 p.m. |
| PD | Predicate disambiguation | batch_69aef90438908190a005b08ba271eacf |
completed | March 9, 2026, 4:44 p.m. |
Created at: March 9, 2026, 3:38 p.m.